Cancer is a global healthcare challenge; the healthcare industry is witnessing rapid new developments, improved point-of-care
diagnostics, cost-effective treatment modalities and personalized medicine, which not only offer efficient early diagnosis but also treat cancer conditions
in order to reduce the healthcare cost and disease burden. Data published last October in Nature Genetics from the TCGA Research Network identified
a distinct class of solid tumors that are characterized by recurrent copy number changes, opening up the possibility for new approaches to therapy and
patient stratification. Recently developed assay technology and algorithms address the challenge of delivering reliable genome-wide copy number detection
from FFPE-derived solid tumor DNA.

Effective administration of targeted therapeutics and monitoring of disease
progression require frequent profiling of a patient’s tumor for molecular changes at the epigenetic, genetic and protein levels. As newer and more
effective therapies are developed and approved, routine profiling will be become essential for therapy selection and patient monitoring.

Cancer is an extremely heterogeneous disease with over 200 known classifications and many more subtypes involving
dysregulation of multiple pathways regulating many fundamental cell processes such as growth, death, proliferation, differentiation and migration, and it
arises as a result of an accumulation of genetic aberrations that are either acquired or inherited. Virtually all cancers have a unique set of molecular
changes and many technologies have been developed to study cancer and derive molecular characteristics that increasingly have implications for understanding
treatment options, prognosis or survival outcome for a patient.

Building upon the foundation laid by researchers,
the National Institutes of Health (NIH) launched The Cancer Genome Atlas (TCGA) Pilot Project in 2006 to further scientific understanding of cancer and
create a comprehensive “atlas” of the genomic changes involved in cancer.

The pilot project began in
2006 as a three-year collaboration with an investment of $50 million each from NIH’s National Cancer Institute (NCI) and National Human Genome Research
Institute (NHGRI). The TCGA Pilot Project confirmed that an atlas of changes could be created for specific cancer types (brain, ovary and lung). It also
showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an
economy of scale and develop an infrastructure for making the data publicly accessible. Importantly, it proved that making the data freely available would
enable researchers anywhere around the world to make and validate important discoveries.

The success of the pilot
led the NIH to commit major resources to TCGA to collect and characterize more than 20 additional tumor types. Each cancer will undergo comprehensive genomic
characterization and analysis. The comprehensive data that have been generated by TCGA’s network approach are freely available and widely used by the
cancer community through the TCGA Data Portal and the Cancer Genomics Hub (CGHub). By the end of 2015, the TCGA Research Network plans to have achieved the
ambitious goal of analyzing the genomic, epigenomic and gene expression profiles of more than 10,000 specimens from more than 25 different tumor
types.

The first group of papers from TCGA Research Network members’ integrated analysis of genomic data
across multiple cancers has been published in last October’s issue of Nature Genetics. The Pan-Cancer Analysis was presented as a series of 18
papers. These papers are part of the Pan-Cancer project, an initiative to assemble and analyze TCGA’s wealth of data across 12 tumor types. Until now,
research has mostly focused on individual cancer types. The Pan-Cancer project represents an approach that examines cancers based not only on their organ of
origin, but also their genomic profiles. This integrated analysis provides a comprehensive picture of the molecular biology of multiple cancers, revealing
insights into similarities and differences in their genomic changes.

There were many significant new findings
reported; a publication by Ciriello. G, at al.1 highlighted that many of the genetic alterations contributing to cancer are shared
between tumors in ways that are independent of the tissue in which the cancer originates and that tumors fell into two broad categories: one characterized by
widespread copy number changes and another marked by a preponderance of somatic mutations. This distinction between these two classes was clearest at the
extremes of genomic instability, indicating the presence of different oncogenic processes. The class of cancers characterized by multiple recurrent
chromosomal gains and losses is named the “C” class of cancers. This class includes almost all serous ovarian and breast carcinoma samples, as
well as a large fraction of lung and head and neck squamous cell carcinomas and endometrioid tumors of the serous subtype. “Druggable”
aberrations within these cancers open up opportunities for addressing these diseases with combination regimens of available therapies; by limiting testing to
somatic mutations or single gene or low-plex copy number testing, these “druggable” markers will be missed.

Progress to assess genome-wide copy number changes to better classify, manage and develop targeted therapies has been hampered by the challenges of
the solid tumor sample type. Firstly, sample heterogeneity: clonal expansion plus genomic instability can result in a highly heterogeneous cellular
population within tumors that can be difficult to identify. This issue can be addressed by looking at the level of the single cell or by digital counting,
but both are time-consuming and costly. A second challenge is the limited quantity of starting DNA from clinical samples such as core needle biopsies and/or
very limited and precious samples. The third challenge is that highly degraded FFPE samples can provide very important information for both basic and
clinical scientists, but older specimens have been very difficult to interrogate with existing microarray solutions or next-generation sequencing (NGS). It
is estimated that about 1 billion FFPE blocks are banked around the world. Using medical records in association with the long lifespan of the blocks enables
highly powered, retrospective studies that can accelerate new drug development if genomic information can be coupled with retrospective drug response, such
as in companion diagnostic tests.

To generate reliable data from this sample type, high depth sequencing and high
tumor burden are required. This challenge has been addressed for mutations by targeted sequencing of specific single - few bps loci sequenced at high
coverage (typically 500x-1000x). However, copy number affects large genomic regions, and this depth of sequencing across entire regions is challenging along
with the requirement to differentiate low-level gains, hemizygous deletions and copy-neutral loss of heterozygosity (LOH)—which all have important and
independent clinical relevance.

The Molecular Inversion Probe (MIP) assay technology in conjunction with recently
introduced high-density SNP arrays has been able to address these challenges. Originally developed for single nucleotide polymorphism (SNP) genotyping, but
subsequently used for identifying other types of genetic variation including focal insertions and deletions, larger copy number alterations (CNAs), LOH and
most recently for somatic mutation detection, the assay requires as little as 80ng of genomic DNA and has been shown to perform well with highly degraded
DNA, such as that from formalin-fixed paraffin-embedded (FFPE)-preserved samples from 20 years ago or older. Central to the MIP assay technology are the
padlock probes that hybridize to the DNA target of interest before polymerase chain reaction amplification, leading to high assay specificity. The MIP assay
has enabled new discoveries and a deeper understanding of the molecular basis of cancer and its various disease subtypes.2 One of many published
papers using the MIP technology highlights this in the case of melanoma where the technology was applied to differentiate benign melanocytic nevi from
malignant melanoma and to predict the clinical course of melanocytic lesions with ambiguous histology.3

Novel algorithms used in combination with MIP assay technology such as those derived from ASCAT 4 have been used to determine if a
consistent percent aberrant cells (%AC) and ploidy are present at each copy number change. The algorithm is also capable of reporting the linear integer copy
number in the cancer portion only, effectively subtracting the normal component, thereby enabling a comparison between tumor samples with different
contributions of normal cell contamination.

In conclusion, whole-genome copy number profiling is emerging as an
increasingly important approach in the quest to discover, develop and deliver new cancer profiling tests to improve understanding and treatment across a
broad spectrum of solid tumor diseases. Recently developed assay technology and algorithms address the challenge of reliable genome-wide copy number
detection from solid tumors.

Lisa Cowell is senior director of
strategic marketing clinical applications at Affymetrix, which is based in Santa Clara, Calif.
She has held various marketing, business development and strategic planning positions during her 14-year tenure at Affymetrix. Prior to joining Affymetrix,
Cowell was vice president of the Pharmacogenetics Business at Amersham Biosciences in the United Kingdom. For more about Affymetrix and its product lines,
you can visit www.affymetrix.com.